No abstract

Purpose To pool data across multiple institutions internationally and report on the cumulative experience of brainstem stereotactic radiosurgery (SRS). Methods and Materials Data on patients with brainstem metastases treated with SRS were collected through the International Gamma Knife Research Foundation. Clinical, radiographic, and dosimetric characteristics were compared for factors prognostic for local control (LC) and overall survival (OS) using univariate and multivariate analyses. Results Of 547 patients with 596 brainstem metastases treated with SRS, treatment of 7.4% of tumors resulted in severe SRS-induced toxicity (grade ≥3, increased odds with increasing tumor volume, margin dose, and whole-brain irradiation). Local control at 12 months after SRS was 81.8% and was improved with increasing margin dose and maximum dose. Overall survival at 12 months after SRS was 32.7% and impacted by age, gender, number of metastases, tumor histology, and performance score. Conclusions Our study provides additional evidence that SRS has become an option for patients with brainstem metastases, with an excellent benefit-to-risk ratio in the hands of experienced clinicians. Prior whole-brain irradiation increases the risk of severe toxicity in brainstem metastasis patients undergoing SRS.

Radiotherapy has been used for more than a hundred years to cure or locally control tumors. Regression of tumors outside of the irradiated field was occasionally observed and is known as the abscopal effect. However, the occurrence of systemic anti-tumor effects was deemed too rare and unpredictable to be a therapeutic goal. Recent studies suggest that immunotherapy and radiation in combination may enhance the abscopal response. Increasing numbers of cases are being reported since the routine implementation of immune checkpoint inhibitors, showing that combined radiotherapy with immunotherapy has a synergistic effect on both local and distant (i.e., unirradiated) tumors. In this review, we summarize pre-clinical and clinical reports, with a specific focus on the mechanisms behind the immunostimulatory effects of radiation and how this is enhanced by immunotherapy.

Recent radiomic studies have witnessed promising performance of deep learning techniques in learning radiomic features and fusing multimodal imaging data. Most existing deep learning based radiomic studies build predictive models in a setting of pattern classification, not appropriate for survival analysis studies where some data samples have incomplete observations. To improve existing survival analysis techniques whose performance is hinged on imaging features, we propose a deep learning method to build survival regression models by optimizing imaging features with deep convolutional neural networks (CNNs) in a proportional hazards model. To make the CNNs applicable to tumors with varied sizes, a spatial pyramid pooling strategy is adopted. Our method has been validated based on a simulated imaging dataset and a FDG-PET/CT dataset of rectal cancer patients treated for locally advanced rectal cancer. Compared with survival prediction models built upon hand-crafted radiomic features using Cox proportional hazards model and random survival forests, our method achieved competitive prediction performance.

Convolutional neural networks (CNNs) have become the state-of-the-art method for medical segmentation. However, repeated pooling and striding operations reduce the feature resolution, causing loss of detailed information. Additionally, tumors of different patients are of different sizes. Thus, small tumors may be ignored while big tumors may exceed the receptive fields of convolutions. The purpose of this study is to further improve the segmentation accuracy using a novel CNN (named CAC-SPP) with cascaded atrous convolution (CAC) and a spatial pyramid pooling (SPP) module. This work is the first attempt at applying SPP for segmentation in radiotherapy. We improved the network based on ResNet-101 yielding accuracy gains from a greatly increased depth. We added CAC to extract a high-resolution feature map while maintaining large receptive fields. We also adopted a parallel SPP module with different atrous rates to capture the multi-scale features. The performance was compared with the widely adopted U-Net and ResNet-101 with independent segmentation of rectal tumors for two image sets, separately: (1) 70 T2-weighted MR images and (2) 100 planning CT images. The results show that the proposed CAC-SPP outperformed the U-Net and ResNet-101 for both image sets. The Dice similarity coefficient values of CAC-SPP were 0.78  ±  0.08 and 0.85  ±  0.03, respectively, which were higher than those of U-Net (0.70  ±  0.11 and 0.82  ±  0.04) and ResNet-101 (0.76  ±  0.10 and 0.84  ±  0.03). The segmentation speed of CAC-SPP was comparable with ResNet-101, but about 36% faster than U-Net. In conclusion, the proposed CAC-SPP, which could extract high-resolution features with large receptive fields and capture multi-scale context yields, improves the accuracy of segmentation performance for rectal tumors.